


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[Any] 			=		BarthezTokenizer
			_UpperCamelCase		:       Optional[int] 			=		BarthezTokenizerFast
			_UpperCamelCase		:       List[str] 			=		True
			_UpperCamelCase		:       Tuple 			=		True




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().setUp()

						UpperCamelCase__       =							BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"  )
						tokenizer.save_pretrained(self.tmpdirname  )
						tokenizer.save_pretrained(self.tmpdirname   ,    legacy_format=snake_case  )
						UpperCamelCase__       =							tokenizer




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"<pad>"
						UpperCamelCase__       =							1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case  )   ,    snake_case  )
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case  )   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							list(self.get_tokenizer().get_vocab().keys()  )

						self.assertEqual(vocab_keys[0]   ,    "<s>"  )
						self.assertEqual(vocab_keys[1]   ,    "<pad>"  )
						self.assertEqual(vocab_keys[-1]   ,    "<mask>"  )
						self.assertEqual(len(snake_case  )   ,    101122  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.assertEqual(self.get_tokenizer().vocab_size   ,    101122  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						UpperCamelCase__       =							[0, 57, 3018, 70307, 91, 2]

						UpperCamelCase__       =							self.tokenizer(
						    snake_case   ,    max_length=len(snake_case  )   ,    padding=snake_case   ,    truncation=snake_case   ,    return_tensors="pt"  )
						self.assertIsInstance(snake_case   ,    snake_case  )

						self.assertEqual((2, 6)   ,    batch.input_ids.shape  )
						self.assertEqual((2, 6)   ,    batch.attention_mask.shape  )
						UpperCamelCase__       =							batch.input_ids.tolist()[0]
						self.assertListEqual(snake_case   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if not self.test_rust_tokenizer:
									return

						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_rust_tokenizer()

						UpperCamelCase__       =							"I was born in 92000, and this is falsé."

						UpperCamelCase__       =							tokenizer.tokenize(snake_case  )
						UpperCamelCase__       =							rust_tokenizer.tokenize(snake_case  )
						self.assertListEqual(snake_case   ,    snake_case  )

						UpperCamelCase__       =							tokenizer.encode(snake_case   ,    add_special_tokens=snake_case  )
						UpperCamelCase__       =							rust_tokenizer.encode(snake_case   ,    add_special_tokens=snake_case  )
						self.assertListEqual(snake_case   ,    snake_case  )

						UpperCamelCase__       =							self.get_rust_tokenizer()
						UpperCamelCase__       =							tokenizer.encode(snake_case  )
						UpperCamelCase__       =							rust_tokenizer.encode(snake_case  )
						self.assertListEqual(snake_case   ,    snake_case  )







			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							{"input_ids": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# moussaKam/mbarthez is a french model. So we also use french texts.
						UpperCamelCase__       =							[
						    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
						    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
						    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
						    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
						    "telles que la traduction et la synthèse de texte.",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=snake_case   ,    model_name="moussaKam/mbarthez"   ,    revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"   ,    sequences=snake_case   ,    )






import requests
from bsa import BeautifulSoup
def 							UpperCamelCase_(					_A :str = "AAPL"	)->		str:
			UpperCamelCase__       =							F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
			UpperCamelCase__       =							BeautifulSoup(requests.get(_A	).text					,  "html.parser"	)
			UpperCamelCase__       =							"My(6px) Pos(r) smartphone_Mt(6px)"
			return soup.find("div"					,  class_=class_	).find("span"	).text


if __name__ == "__main__":
						for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
												print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')


